2025Farheen Modeling: Difference between revisions
Created page with "== Citation == F. Farheen, G. Terashi, H. Zhu, and D. Kihara, “AI-based methods for biomolecular structure modeling for Cryo-EM,” Current Opinion in Structural Biology, vol. 90, p. 102989, 2025. == Abstract == Cryo-electronmicroscopy (Cryo-EM) has revolutionized structural biology by enabling the determination of macromolecular structures that were challenging to study with conventional methods. Processing cryo-EM data involves several computational steps to deriv..." |
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Latest revision as of 11:51, 4 March 2025
Citation
F. Farheen, G. Terashi, H. Zhu, and D. Kihara, “AI-based methods for biomolecular structure modeling for Cryo-EM,” Current Opinion in Structural Biology, vol. 90, p. 102989, 2025.
Abstract
Cryo-electronmicroscopy (Cryo-EM) has revolutionized structural biology by enabling the determination of macromolecular structures that were challenging to study with conventional methods. Processing cryo-EM data involves several computational steps to derive three-dimensional structures from raw projections. Recent advancements in artificial intelligence (AI) including deep learning have significantly improved the performance of these processes. In this review, we discuss state-of-the-art AI-based techniques used in key steps of cryo-EM data processing, including macromolecular structure modeling and heterogeneity analysis.
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Links
https://www.sciencedirect.com/science/article/pii/S0959440X25000077